With the advancement of mobile devices and machine automation, new ways of interfacing with technology are emerging. Users now have the ability to interact with automated systems in many environments.
One such interaction can take place in the field of automated compliance and monitoring systems, such as hand washing compliance systems in restaurants and hospitals. These automated systems can reduce many expenses including costs due to human error, employment costs, and costs of complexity arising from lack of standardization.
Automated systems of this kind are increasingly seen as invaluable in environments where compliance is crucial and alternatives are difficult and expensive to implement. Illustratively, it has been reported that more than 50% of all nosocomial infections can be directly related to the transmission of harmful bacteria by healthcare workers who have not properly washed their hands before and after each patient contact.
Thus, the best means to prevent transfer of these organisms from patient to patient and to reduce the emergence of resistant organisms is hand-washing with soap and water between patient contacts. The Centers for Disease Control and Prevention as well as other regulatory agencies recommend hand-washing before and after each patient encounter.
Automated compliance systems in a healthcare environment can increase compliance rates noticeably; however, the implementation of automated compliance systems is fraught with difficulties. Chief among these difficulties is distinguishing between multiple active users of the automated system in a streamlined and intuitive way that does not prohibitively increase the workload of the users, which can lead to an aversion to using the system.
The inability of automated systems to distinguish between multiple users can arise when an automated system is able to sense the presence of multiple users but the system is not able to sense other attributes of the users that would correlate with the user interacting with the system rather than being merely proximally close to the system.
Many previous developments have been advanced to address this problem. For example, some automated systems attempt to triangulate a user's position relative to a station. This triangulation method introduces the additional problems of requiring expensive and sophisticated antenna and power hungry components, both of which reduce the effectiveness and ability of the system to be implemented and adopted. Further the triangulation method can only provide partial results with substandard accuracy.
Effective solutions have been long sought but prior developments have not taught or suggested any, and solutions to these problems have long eluded those skilled in the art. Thus there remains a considerable need for devices and methods that can identify and disambiguate between multiple users.